Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations720
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory90.1 KiB
Average record size in memory128.2 B

Variable types

DateTime1
Categorical4
Numeric11

Alerts

Business_Unit is highly overall correlated with City and 1 other fieldsHigh correlation
City is highly overall correlated with Business_Unit and 1 other fieldsHigh correlation
Costs_DKK is highly overall correlated with Guests and 7 other fieldsHigh correlation
Guests is highly overall correlated with Costs_DKK and 7 other fieldsHigh correlation
Hours_Worked is highly overall correlated with Costs_DKK and 7 other fieldsHigh correlation
Local_Supplier_% is highly overall correlated with TypeHigh correlation
Organic_Percentage is highly overall correlated with Costs_DKK and 6 other fieldsHigh correlation
Profit_DKK is highly overall correlated with Costs_DKK and 6 other fieldsHigh correlation
Revenue_DKK is highly overall correlated with Costs_DKK and 7 other fieldsHigh correlation
Revenue_per_Hour is highly overall correlated with Costs_DKK and 7 other fieldsHigh correlation
Type is highly overall correlated with Business_Unit and 10 other fieldsHigh correlation
Waste_kg is highly overall correlated with Costs_DKK and 7 other fieldsHigh correlation
Business_Unit is uniformly distributedUniform
Revenue_DKK has unique valuesUnique
Costs_DKK has unique valuesUnique
Profit_DKK has unique valuesUnique

Reproduction

Analysis started2025-10-07 16:25:35.244351
Analysis finished2025-10-07 16:26:02.109154
Duration26.86 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Date
Date

Distinct90
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Minimum1970-01-01 01:00:45.658000
Maximum1970-01-01 01:00:45.747000
Invalid dates0
Invalid dates (%)0.0%
2025-10-07T18:26:02.387991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:26:02.722132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Business_Unit
Categorical

High correlation  Uniform 

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Almanak i Operaen
90 
Almanak i Kilden
90 
Radio
90 
Format
90 
Posthallen
90 
Other values (3)
270 

Length

Max length18
Median length16.5
Mean length13.125
Min length5

Characters and Unicode

Total characters9450
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlmanak i Operaen
2nd rowAlmanak i Kilden
3rd rowRadio
4th rowFormat
5th rowPosthallen

Common Values

ValueCountFrequency (%)
Almanak i Operaen90
12.5%
Almanak i Kilden90
12.5%
Radio90
12.5%
Format90
12.5%
Posthallen90
12.5%
Langelinie Kantine90
12.5%
Bankdata Kantine90
12.5%
Carlsberg Kantine90
12.5%

Length

2025-10-07T18:26:03.023117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-07T18:26:03.342664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kantine270
20.0%
i180
13.3%
almanak180
13.3%
operaen90
 
6.7%
kilden90
 
6.7%
format90
 
6.7%
radio90
 
6.7%
posthallen90
 
6.7%
langelinie90
 
6.7%
bankdata90
 
6.7%

Most occurring characters

ValueCountFrequency (%)
a1440
15.2%
n1260
13.3%
e900
 
9.5%
i810
 
8.6%
630
 
6.7%
l630
 
6.7%
t540
 
5.7%
K360
 
3.8%
r360
 
3.8%
k270
 
2.9%
Other values (16)2250
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1440
15.2%
n1260
13.3%
e900
 
9.5%
i810
 
8.6%
630
 
6.7%
l630
 
6.7%
t540
 
5.7%
K360
 
3.8%
r360
 
3.8%
k270
 
2.9%
Other values (16)2250
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1440
15.2%
n1260
13.3%
e900
 
9.5%
i810
 
8.6%
630
 
6.7%
l630
 
6.7%
t540
 
5.7%
K360
 
3.8%
r360
 
3.8%
k270
 
2.9%
Other values (16)2250
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1440
15.2%
n1260
13.3%
e900
 
9.5%
i810
 
8.6%
630
 
6.7%
l630
 
6.7%
t540
 
5.7%
K360
 
3.8%
r360
 
3.8%
k270
 
2.9%
Other values (16)2250
23.8%

Type
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Restaurant
450 
Canteen
270 

Length

Max length10
Median length10
Mean length8.875
Min length7

Characters and Unicode

Total characters6390
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRestaurant
2nd rowRestaurant
3rd rowRestaurant
4th rowRestaurant
5th rowRestaurant

Common Values

ValueCountFrequency (%)
Restaurant450
62.5%
Canteen270
37.5%

Length

2025-10-07T18:26:03.801704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-07T18:26:03.975483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
restaurant450
62.5%
canteen270
37.5%

Most occurring characters

ValueCountFrequency (%)
a1170
18.3%
t1170
18.3%
e990
15.5%
n990
15.5%
R450
 
7.0%
s450
 
7.0%
u450
 
7.0%
r450
 
7.0%
C270
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)6390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1170
18.3%
t1170
18.3%
e990
15.5%
n990
15.5%
R450
 
7.0%
s450
 
7.0%
u450
 
7.0%
r450
 
7.0%
C270
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1170
18.3%
t1170
18.3%
e990
15.5%
n990
15.5%
R450
 
7.0%
s450
 
7.0%
u450
 
7.0%
r450
 
7.0%
C270
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1170
18.3%
t1170
18.3%
e990
15.5%
n990
15.5%
R450
 
7.0%
s450
 
7.0%
u450
 
7.0%
r450
 
7.0%
C270
 
4.2%

City
Categorical

High correlation 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Copenhagen
450 
Hellerup
90 
Fredericia
90 
Valby
90 

Length

Max length10
Median length10
Mean length9.125
Min length5

Characters and Unicode

Total characters6570
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCopenhagen
2nd rowHellerup
3rd rowCopenhagen
4th rowCopenhagen
5th rowCopenhagen

Common Values

ValueCountFrequency (%)
Copenhagen450
62.5%
Hellerup90
 
12.5%
Fredericia90
 
12.5%
Valby90
 
12.5%

Length

2025-10-07T18:26:04.180184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-07T18:26:04.366175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
copenhagen450
62.5%
hellerup90
 
12.5%
fredericia90
 
12.5%
valby90
 
12.5%

Most occurring characters

ValueCountFrequency (%)
e1260
19.2%
n900
13.7%
a630
9.6%
p540
8.2%
C450
 
6.8%
o450
 
6.8%
h450
 
6.8%
g450
 
6.8%
l270
 
4.1%
r270
 
4.1%
Other values (9)900
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1260
19.2%
n900
13.7%
a630
9.6%
p540
8.2%
C450
 
6.8%
o450
 
6.8%
h450
 
6.8%
g450
 
6.8%
l270
 
4.1%
r270
 
4.1%
Other values (9)900
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1260
19.2%
n900
13.7%
a630
9.6%
p540
8.2%
C450
 
6.8%
o450
 
6.8%
h450
 
6.8%
g450
 
6.8%
l270
 
4.1%
r270
 
4.1%
Other values (9)900
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1260
19.2%
n900
13.7%
a630
9.6%
p540
8.2%
C450
 
6.8%
o450
 
6.8%
h450
 
6.8%
g450
 
6.8%
l270
 
4.1%
r270
 
4.1%
Other values (9)900
13.7%

Revenue_DKK
Real number (ℝ)

High correlation  Unique 

Distinct720
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42515.392
Minimum15013.94
Maximum69963.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:04.668192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15013.94
5-th percentile16699.644
Q124657.052
median45687.51
Q357397.098
95-th percentile67288.976
Maximum69963.79
Range54949.85
Interquartile range (IQR)32740.045

Descriptive statistics

Standard deviation17266.986
Coefficient of variation (CV)0.40613493
Kurtosis-1.4137274
Mean42515.392
Median Absolute Deviation (MAD)16561.215
Skewness-0.14352654
Sum30611082
Variance2.981488 × 108
MonotonicityNot monotonic
2025-10-07T18:26:05.201507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15835.921
 
0.1%
51236.21
 
0.1%
65985.281
 
0.1%
45454.751
 
0.1%
41399.971
 
0.1%
65798.211
 
0.1%
18615.381
 
0.1%
19675.671
 
0.1%
28422.411
 
0.1%
51660.321
 
0.1%
Other values (710)710
98.6%
ValueCountFrequency (%)
15013.941
0.1%
15096.971
0.1%
15110.011
0.1%
15148.861
0.1%
15181.551
0.1%
15186.061
0.1%
15370.261
0.1%
15381.291
0.1%
15406.61
0.1%
15407.511
0.1%
ValueCountFrequency (%)
69963.791
0.1%
69917.671
0.1%
69915.471
0.1%
69896.971
0.1%
69824.181
0.1%
69797.961
0.1%
69788.941
0.1%
69715.151
0.1%
69692.691
0.1%
69545.751
0.1%

Costs_DKK
Real number (ℝ)

High correlation  Unique 

Distinct720
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29967.933
Minimum9333.88
Maximum56986.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:05.512816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9333.88
5-th percentile11501.876
Q117001.667
median31950.14
Q340193.217
95-th percentile49538.32
Maximum56986.61
Range47652.73
Interquartile range (IQR)23191.55

Descriptive statistics

Standard deviation12559.519
Coefficient of variation (CV)0.4190986
Kurtosis-1.286777
Mean29967.933
Median Absolute Deviation (MAD)11623.36
Skewness-0.04087723
Sum21576912
Variance1.5774151 × 108
MonotonicityNot monotonic
2025-10-07T18:26:05.830629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11022.381
 
0.1%
36857.791
 
0.1%
40335.641
 
0.1%
27337.091
 
0.1%
26493.281
 
0.1%
51965.921
 
0.1%
11297.261
 
0.1%
11928.621
 
0.1%
20296.121
 
0.1%
40969.271
 
0.1%
Other values (710)710
98.6%
ValueCountFrequency (%)
9333.881
0.1%
9830.381
0.1%
9854.021
0.1%
10014.411
0.1%
10114.821
0.1%
10210.771
0.1%
10255.111
0.1%
10280.131
0.1%
10294.11
0.1%
10320.381
0.1%
ValueCountFrequency (%)
56986.611
0.1%
54648.591
0.1%
54290.291
0.1%
54198.711
0.1%
54053.451
0.1%
53536.531
0.1%
52760.441
0.1%
52420.91
0.1%
51965.921
0.1%
51591.81
0.1%

Guests
Real number (ℝ)

High correlation 

Distinct213
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.27639
Minimum80
Maximum319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:06.139539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile91
Q1130
median215
Q3266
95-th percentile309
Maximum319
Range239
Interquartile range (IQR)136

Descriptive statistics

Standard deviation74.08481
Coefficient of variation (CV)0.36625535
Kurtosis-1.3768627
Mean202.27639
Median Absolute Deviation (MAD)68
Skewness-0.12977058
Sum145639
Variance5488.5591
MonotonicityNot monotonic
2025-10-07T18:26:06.446322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2338
 
1.1%
3087
 
1.0%
3027
 
1.0%
1347
 
1.0%
1397
 
1.0%
1097
 
1.0%
917
 
1.0%
997
 
1.0%
2427
 
1.0%
3127
 
1.0%
Other values (203)649
90.1%
ValueCountFrequency (%)
801
 
0.1%
812
 
0.3%
826
0.8%
832
 
0.3%
844
0.6%
855
0.7%
861
 
0.1%
876
0.8%
882
 
0.3%
894
0.6%
ValueCountFrequency (%)
3195
0.7%
3181
 
0.1%
3173
0.4%
3163
0.4%
3152
 
0.3%
3143
0.4%
3135
0.7%
3127
1.0%
3113
0.4%
3102
 
0.3%

Waste_kg
Real number (ℝ)

High correlation 

Distinct625
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.285431
Minimum1.5
Maximum31.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:06.760312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile6.019
Q110.81
median16.67
Q321.4225
95-th percentile25.8835
Maximum31.54
Range30.04
Interquartile range (IQR)10.6125

Descriptive statistics

Standard deviation6.3264704
Coefficient of variation (CV)0.38847425
Kurtosis-1.0358985
Mean16.285431
Median Absolute Deviation (MAD)5.305
Skewness-0.094736005
Sum11725.51
Variance40.024228
MonotonicityNot monotonic
2025-10-07T18:26:07.072156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.374
 
0.6%
21.853
 
0.4%
16.033
 
0.4%
20.943
 
0.4%
20.123
 
0.4%
26.593
 
0.4%
11.763
 
0.4%
23.973
 
0.4%
10.923
 
0.4%
21.832
 
0.3%
Other values (615)690
95.8%
ValueCountFrequency (%)
1.51
0.1%
1.751
0.1%
3.021
0.1%
3.311
0.1%
3.361
0.1%
3.761
0.1%
3.971
0.1%
4.211
0.1%
4.751
0.1%
4.821
0.1%
ValueCountFrequency (%)
31.541
0.1%
29.461
0.1%
28.491
0.1%
28.151
0.1%
27.81
0.1%
27.781
0.1%
27.761
0.1%
27.71
0.1%
27.631
0.1%
27.551
0.1%

Hours_Worked
Real number (ℝ)

High correlation 

Distinct478
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.33
Minimum76.1
Maximum193.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:07.376309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum76.1
5-th percentile98.895
Q1114.85
median137.1
Q3154.2
95-th percentile171.21
Maximum193.3
Range117.2
Interquartile range (IQR)39.35

Descriptive statistics

Standard deviation23.683468
Coefficient of variation (CV)0.17500531
Kurtosis-0.94286635
Mean135.33
Median Absolute Deviation (MAD)19.6
Skewness-0.055959351
Sum97437.6
Variance560.90666
MonotonicityNot monotonic
2025-10-07T18:26:07.693925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156.96
 
0.8%
118.64
 
0.6%
145.94
 
0.6%
142.74
 
0.6%
108.14
 
0.6%
152.24
 
0.6%
115.84
 
0.6%
105.34
 
0.6%
119.44
 
0.6%
136.94
 
0.6%
Other values (468)678
94.2%
ValueCountFrequency (%)
76.11
0.1%
76.61
0.1%
80.91
0.1%
85.71
0.1%
86.11
0.1%
86.81
0.1%
88.61
0.1%
89.31
0.1%
90.31
0.1%
90.41
0.1%
ValueCountFrequency (%)
193.31
0.1%
190.31
0.1%
189.11
0.1%
188.41
0.1%
183.71
0.1%
180.92
0.3%
180.61
0.1%
180.11
0.1%
179.31
0.1%
179.11
0.1%

Organic_Percentage
Real number (ℝ)

High correlation 

Distinct266
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.109722
Minimum60.1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:08.019303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60.1
5-th percentile62.4
Q171.175
median76.3
Q382.325
95-th percentile88.305
Maximum90
Range29.9
Interquartile range (IQR)11.15

Descriptive statistics

Standard deviation7.7317808
Coefficient of variation (CV)0.10158729
Kurtosis-0.77757142
Mean76.109722
Median Absolute Deviation (MAD)5.55
Skewness-0.14244376
Sum54799
Variance59.780434
MonotonicityNot monotonic
2025-10-07T18:26:08.492270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.98
 
1.1%
79.78
 
1.1%
76.38
 
1.1%
73.77
 
1.0%
73.27
 
1.0%
76.57
 
1.0%
77.17
 
1.0%
70.96
 
0.8%
79.66
 
0.8%
77.56
 
0.8%
Other values (256)650
90.3%
ValueCountFrequency (%)
60.12
 
0.3%
60.23
0.4%
60.31
 
0.1%
60.46
0.8%
60.51
 
0.1%
60.63
0.4%
60.83
0.4%
61.22
 
0.3%
61.31
 
0.1%
61.44
0.6%
ValueCountFrequency (%)
902
 
0.3%
89.95
0.7%
89.81
 
0.1%
89.71
 
0.1%
89.61
 
0.1%
89.53
0.4%
89.41
 
0.1%
89.31
 
0.1%
89.23
0.4%
892
 
0.3%

Local_Supplier_%
Real number (ℝ)

High correlation 

Distinct298
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.755
Minimum50.1
Maximum84.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:08.863350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.1
5-th percentile53.395
Q162.175
median69.05
Q375.425
95-th percentile82.705
Maximum84.9
Range34.8
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation8.8426173
Coefficient of variation (CV)0.12861053
Kurtosis-0.84724855
Mean68.755
Median Absolute Deviation (MAD)6.65
Skewness-0.089791572
Sum49503.6
Variance78.19188
MonotonicityNot monotonic
2025-10-07T18:26:09.215217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.98
 
1.1%
70.17
 
1.0%
72.67
 
1.0%
74.66
 
0.8%
69.66
 
0.8%
81.96
 
0.8%
70.56
 
0.8%
80.76
 
0.8%
61.36
 
0.8%
64.56
 
0.8%
Other values (288)656
91.1%
ValueCountFrequency (%)
50.11
0.1%
50.32
0.3%
50.51
0.1%
50.71
0.1%
50.81
0.1%
50.91
0.1%
512
0.3%
51.12
0.3%
51.31
0.1%
51.41
0.1%
ValueCountFrequency (%)
84.94
0.6%
84.81
 
0.1%
84.71
 
0.1%
84.61
 
0.1%
84.53
0.4%
84.41
 
0.1%
84.31
 
0.1%
84.22
0.3%
84.11
 
0.1%
83.93
0.4%

Employee_Count
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
11
154 
14
154 
13
153 
12
142 
10
117 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1440
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row11
3rd row11
4th row12
5th row13

Common Values

ValueCountFrequency (%)
11154
21.4%
14154
21.4%
13153
21.2%
12142
19.7%
10117
16.2%

Length

2025-10-07T18:26:09.511492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-07T18:26:09.710275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11154
21.4%
14154
21.4%
13153
21.2%
12142
19.7%
10117
16.2%

Most occurring characters

ValueCountFrequency (%)
1874
60.7%
4154
 
10.7%
3153
 
10.6%
2142
 
9.9%
0117
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1874
60.7%
4154
 
10.7%
3153
 
10.6%
2142
 
9.9%
0117
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1874
60.7%
4154
 
10.7%
3153
 
10.6%
2142
 
9.9%
0117
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1874
60.7%
4154
 
10.7%
3153
 
10.6%
2142
 
9.9%
0117
 
8.1%

Profit_DKK
Real number (ℝ)

High correlation  Unique 

Distinct720
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12547.46
Minimum3153.31
Maximum26987.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:10.016269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3153.31
5-th percentile4777.5125
Q17368.455
median12338.07
Q316568.005
95-th percentile22635.718
Maximum26987.32
Range23834.01
Interquartile range (IQR)9199.55

Descriptive statistics

Standard deviation5650.6233
Coefficient of variation (CV)0.45034003
Kurtosis-0.85515324
Mean12547.46
Median Absolute Deviation (MAD)4631.555
Skewness0.299475
Sum9034170.9
Variance31929544
MonotonicityNot monotonic
2025-10-07T18:26:10.340338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4813.541
 
0.1%
14378.411
 
0.1%
25649.641
 
0.1%
18117.661
 
0.1%
14906.691
 
0.1%
13832.291
 
0.1%
7318.121
 
0.1%
7747.051
 
0.1%
8126.291
 
0.1%
10691.051
 
0.1%
Other values (710)710
98.6%
ValueCountFrequency (%)
3153.311
0.1%
3237.441
0.1%
3342.991
0.1%
3376.381
0.1%
3410.081
0.1%
3483.311
0.1%
3695.721
0.1%
3722.851
0.1%
3826.071
0.1%
3833.121
0.1%
ValueCountFrequency (%)
26987.321
0.1%
26840.851
0.1%
25767.591
0.1%
25722.461
0.1%
25698.181
0.1%
25649.641
0.1%
25248.881
0.1%
25006.941
0.1%
24896.81
0.1%
24890.171
0.1%

Profit_Margin_%
Real number (ℝ)

Distinct600
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.652764
Minimum18.01
Maximum39.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:10.652918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.01
5-th percentile20.7985
Q124.8275
median29.43
Q334.665
95-th percentile38.7505
Maximum39.96
Range21.95
Interquartile range (IQR)9.8375

Descriptive statistics

Standard deviation5.7938964
Coefficient of variation (CV)0.19539145
Kurtosis-1.1423309
Mean29.652764
Median Absolute Deviation (MAD)4.9
Skewness0.043397411
Sum21349.99
Variance33.569235
MonotonicityNot monotonic
2025-10-07T18:26:10.971407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.195
 
0.7%
32.574
 
0.6%
23.554
 
0.6%
30.293
 
0.4%
25.383
 
0.4%
36.623
 
0.4%
35.83
 
0.4%
25.653
 
0.4%
25.532
 
0.3%
22.622
 
0.3%
Other values (590)688
95.6%
ValueCountFrequency (%)
18.011
0.1%
18.041
0.1%
18.231
0.1%
18.511
0.1%
18.741
0.1%
18.781
0.1%
19.051
0.1%
19.111
0.1%
19.321
0.1%
19.71
0.1%
ValueCountFrequency (%)
39.961
0.1%
39.921
0.1%
39.91
0.1%
39.891
0.1%
39.861
0.1%
39.851
0.1%
39.821
0.1%
39.761
0.1%
39.722
0.3%
39.711
0.1%

Waste_per_Guest_kg
Real number (ℝ)

Distinct79
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.080511111
Minimum0.016
Maximum0.133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:11.513871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.016
5-th percentile0.06
Q10.073
median0.081
Q30.08725
95-th percentile0.10205
Maximum0.133
Range0.117
Interquartile range (IQR)0.01425

Descriptive statistics

Standard deviation0.013449949
Coefficient of variation (CV)0.16705705
Kurtosis2.3279651
Mean0.080511111
Median Absolute Deviation (MAD)0.007
Skewness-0.17394289
Sum57.968
Variance0.00018090113
MonotonicityNot monotonic
2025-10-07T18:26:11.948133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08135
 
4.9%
0.07331
 
4.3%
0.08229
 
4.0%
0.08528
 
3.9%
0.07627
 
3.8%
0.08427
 
3.8%
0.0827
 
3.8%
0.07826
 
3.6%
0.08725
 
3.5%
0.07425
 
3.5%
Other values (69)440
61.1%
ValueCountFrequency (%)
0.0161
 
0.1%
0.021
 
0.1%
0.0321
 
0.1%
0.0361
 
0.1%
0.0372
0.3%
0.0392
0.3%
0.0451
 
0.1%
0.0473
0.4%
0.0481
 
0.1%
0.0492
0.3%
ValueCountFrequency (%)
0.1331
 
0.1%
0.1271
 
0.1%
0.1221
 
0.1%
0.122
0.3%
0.1172
0.3%
0.1152
0.3%
0.1142
0.3%
0.1133
0.4%
0.1111
 
0.1%
0.112
0.3%

Revenue_per_Hour
Real number (ℝ)

High correlation 

Distinct708
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.7624
Minimum120.52
Maximum649.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2025-10-07T18:26:12.333694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum120.52
5-th percentile149.755
Q1222.8325
median307.805
Q3380.82
95-th percentile467.0395
Maximum649.89
Range529.37
Interquartile range (IQR)157.9875

Descriptive statistics

Standard deviation98.871802
Coefficient of variation (CV)0.32336154
Kurtosis-0.71706509
Mean305.7624
Median Absolute Deviation (MAD)78.605
Skewness0.14172618
Sum220148.93
Variance9775.6332
MonotonicityNot monotonic
2025-10-07T18:26:12.703084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
355.732
 
0.3%
265.552
 
0.3%
164.512
 
0.3%
163.842
 
0.3%
251.962
 
0.3%
270.312
 
0.3%
217.942
 
0.3%
257.682
 
0.3%
156.652
 
0.3%
223.362
 
0.3%
Other values (698)700
97.2%
ValueCountFrequency (%)
120.521
0.1%
124.81
0.1%
125.711
0.1%
128.51
0.1%
131.381
0.1%
132.361
0.1%
132.831
0.1%
134.491
0.1%
134.761
0.1%
135.211
0.1%
ValueCountFrequency (%)
649.891
0.1%
567.221
0.1%
557.411
0.1%
541.641
0.1%
531.431
0.1%
5301
0.1%
528.971
0.1%
524.81
0.1%
512.741
0.1%
507.721
0.1%

Interactions

2025-10-07T18:25:58.177496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:35.884654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:37.312712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:38.940516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.852583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.926261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:42.001870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:45.462108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:48.810654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:51.875054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:54.776510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:58.461678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:35.999125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:37.481222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.027774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.943680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.020357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:42.295807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:45.788441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:49.094423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:52.165620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:55.070516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:58.742532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:36.098968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:37.629289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.111684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.032850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.129555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:42.591527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:46.100957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:49.388138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:52.407681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:55.358920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:58.996953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:36.203789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:37.761996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.192293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.128518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.213390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:42.862068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:46.396583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:49.619161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:52.673696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:55.865628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:59.259548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:36.325008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:37.931081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.282370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.213226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.305732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:43.151484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:46.699856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:49.927986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:52.969280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:56.155885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:59.551868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:36.424174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:38.059750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.371287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.360350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.382541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:43.461347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:47.016895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:50.227891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:53.271198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:56.446604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:59.814952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:36.522680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:38.176379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.454936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.468495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.487440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:43.714903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:47.326608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:50.534317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:53.522447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:56.747110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:26:00.126608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:36.642975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:38.370815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.545925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.562847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.590464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:44.234129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:47.606883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:50.828136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:53.778580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:57.037735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:26:00.367106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:36.735566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:38.521831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.621053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.633585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.671341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:44.500042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:47.896982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:51.085371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:54.068955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:57.327678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:26:00.613174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:36.856455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:38.627962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.694166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.722737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.746505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:44.813993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:48.212204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:51.353291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:54.297641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:57.592643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:26:00.887170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:36.996368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:38.840492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:39.779278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:40.820948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:41.825514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:45.146900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:48.533779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:51.632668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:54.535697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-07T18:25:57.873465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-07T18:26:13.035073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Business_UnitCityCosts_DKKEmployee_CountGuestsHours_WorkedLocal_Supplier_%Organic_PercentageProfit_DKKProfit_Margin_%Revenue_DKKRevenue_per_HourTypeWaste_kgWaste_per_Guest_kg
Business_Unit1.0000.9970.3800.0000.3810.3360.2370.2550.3380.0000.3780.3360.9960.3450.134
City0.9971.0000.4350.0000.4490.3800.2690.3150.3850.0000.4340.3870.7550.4060.187
Costs_DKK0.3800.4351.0000.0530.7130.6950.4800.5590.759-0.2770.9690.9230.9940.695-0.023
Employee_Count0.0000.0000.0531.0000.0000.0510.0640.0000.0000.0560.0000.0850.0310.0000.000
Guests0.3810.4490.7130.0001.0000.7040.4790.5160.699-0.0580.7150.6830.9940.9410.008
Hours_Worked0.3360.3800.6950.0510.7041.0000.4710.5210.678-0.0610.6960.5060.8930.684-0.020
Local_Supplier_%0.2370.2690.4800.0640.4790.4711.0000.3830.453-0.0530.4730.4530.6090.4770.002
Organic_Percentage0.2550.3150.5590.0000.5160.5210.3831.0000.491-0.1190.5360.5250.6960.507-0.006
Profit_DKK0.3380.3850.7590.0000.6990.6780.4530.4911.0000.3690.8820.8450.9000.688-0.019
Profit_Margin_%0.0000.000-0.2770.056-0.058-0.061-0.053-0.1190.3691.000-0.051-0.0460.000-0.0410.020
Revenue_DKK0.3780.4340.9690.0000.7150.6960.4730.5360.882-0.0511.0000.9510.9950.700-0.020
Revenue_per_Hour0.3360.3870.9230.0850.6830.5060.4530.5250.845-0.0460.9511.0000.8680.670-0.024
Type0.9960.7550.9940.0310.9940.8930.6090.6960.9000.0000.9950.8681.0000.9090.391
Waste_kg0.3450.4060.6950.0000.9410.6840.4770.5070.688-0.0410.7000.6700.9091.0000.309
Waste_per_Guest_kg0.1340.187-0.0230.0000.008-0.0200.002-0.006-0.0190.020-0.020-0.0240.3910.3091.000

Missing values

2025-10-07T18:26:01.313079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-07T18:26:01.748994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateBusiness_UnitTypeCityRevenue_DKKCosts_DKKGuestsWaste_kgHours_WorkedOrganic_PercentageLocal_Supplier_%Employee_CountProfit_DKKProfit_Margin_%Waste_per_Guest_kgRevenue_per_Hour
01970-01-01 01:00:45.658Almanak i OperaenRestaurantCopenhagen51236.2036857.7927219.93148.173.779.51214378.4128.060.073345.96
11970-01-01 01:00:45.658Almanak i KildenRestaurantHellerup65985.2840335.6427922.79151.872.976.31125649.6438.870.082434.69
21970-01-01 01:00:45.658RadioRestaurantCopenhagen45454.7527337.0920016.04143.682.375.31118117.6639.860.080316.54
31970-01-01 01:00:45.658FormatRestaurantCopenhagen41399.9726493.2819420.64155.979.179.61214906.6936.010.106265.55
41970-01-01 01:00:45.658PosthallenRestaurantCopenhagen65798.2151965.9231423.92145.673.461.61313832.2921.020.076451.91
51970-01-01 01:00:45.658Langelinie KantineCanteenCopenhagen18615.3811297.2613910.66113.962.462.4137318.1239.310.077163.44
61970-01-01 01:00:45.658Bankdata KantineCanteenFredericia19675.6711928.62856.45122.064.264.2117747.0539.370.076161.28
71970-01-01 01:00:45.658Carlsberg KantineCanteenValby28422.4120296.12938.57110.374.558.2148126.2928.590.092257.68
81970-01-01 01:00:45.659Almanak i OperaenRestaurantCopenhagen51660.3240969.2726117.86160.880.874.71410691.0520.690.068321.27
91970-01-01 01:00:45.659Almanak i KildenRestaurantHellerup64065.9143499.2324420.79136.470.370.61220566.6832.100.085469.69
DateBusiness_UnitTypeCityRevenue_DKKCosts_DKKGuestsWaste_kgHours_WorkedOrganic_PercentageLocal_Supplier_%Employee_CountProfit_DKKProfit_Margin_%Waste_per_Guest_kgRevenue_per_Hour
7101970-01-01 01:00:45.746Bankdata KantineCanteenFredericia17066.0112485.32888.79117.060.453.6114580.6926.840.100145.86
7111970-01-01 01:00:45.746Carlsberg KantineCanteenValby19588.4214620.3014810.9299.268.967.7144968.1225.360.074197.46
7121970-01-01 01:00:45.747Almanak i OperaenRestaurantCopenhagen55001.0238220.5829723.20169.480.784.91216780.4430.510.078324.68
7131970-01-01 01:00:45.747Almanak i KildenRestaurantHellerup51708.8738309.0822815.91177.587.770.31413399.7925.910.070291.32
7141970-01-01 01:00:45.747RadioRestaurantCopenhagen58803.1240570.3522515.94142.782.966.91118232.7731.010.071412.08
7151970-01-01 01:00:45.747FormatRestaurantCopenhagen44875.3332887.6925220.82152.173.877.91111987.6426.710.083295.04
7161970-01-01 01:00:45.747PosthallenRestaurantCopenhagen53058.7836320.7226220.05175.980.460.21216738.0631.550.077301.64
7171970-01-01 01:00:45.747Langelinie KantineCanteenCopenhagen19037.8512225.20958.8896.876.364.7146812.6535.780.093196.67
7181970-01-01 01:00:45.747Bankdata KantineCanteenFredericia23673.2817372.72845.3680.965.271.7116300.5626.610.064292.62
7191970-01-01 01:00:45.747Carlsberg KantineCanteenValby15835.9211022.3810513.94104.167.274.9104813.5430.400.133152.12